Real-time non-rigid target tracking for ultrasound-guided clinical interventions

Biological motion is a problem for non-or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of 7 healthy volunteers. Contact echography was performed on all volunteers, while 3 of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non-or mini-invasive clinical intervention. The method demonstrated on average an accuracy of ∼1.5 mm and submillimeter precision. This, together with a computational performance of 20 images/s make the proposed method an attractive solution for real-time target tracking during US-guided clinical interventions